Machine-Learning Techniques for Detecting Attacks in SDN
This work addresses security vulnerabilities in SDNs, which are critical for scalable and manageable cloud computing, but it is incremental as it builds upon existing methods.
The paper conducted a systematic benchmarking analysis of existing machine-learning techniques for detecting malicious traffic in Software Defined Networks (SDNs), identifying limitations and laying the foundation for a more robust framework based on experiments using a publicly available Intrusion Detection Systems dataset.
With the advent of Software Defined Networks (SDNs), there has been a rapid advancement in the area of cloud computing. It is now scalable, cheaper, and easier to manage. However, SDNs are more prone to security vulnerabilities as compared to legacy systems. Therefore, machine-learning techniques are now deployed in the SDN infrastructure for the detection of malicious traffic. In this paper, we provide a systematic benchmarking analysis of the existing machine-learning techniques for the detection of malicious traffic in SDNs. We identify the limitations in these classical machine-learning based methods, and lay the foundation for a more robust framework. Our experiments are performed on a publicly available dataset of Intrusion Detection Systems (IDSs).